The invention relates generally to a method and apparatus for determining the predictability of elements in a weather radar image. In particular, the invention relates to a method for generating a predictability score indicative of the predictability for a pixel in the weather radar image.
Short term (e.g., 30-120 minutes) weather predictions of the location of storms are extremely important to many sectors of the population. For example, aviation systems, traffic information systems, power companies and commuters realize important safety and economic benefits from accurate predictions of organized storms. Unfortunately, the classic weather problem of determining the motion of organized storms has not led to accurate automated forecasts for the short term time scale.
Organized storms are large scale meteorological events, often resulting from frontal discontinuities or cyclonic disturbances. Organized storms include clusters or groups of cells which occur alone or in larger line storms, hurricanes, tropical storms or in association with low pressure centers. Large single cells, including hailstorms, tornadic cells and supercells, are also considered organized storms due to their longer lifetime and broader geographic range. Conversely, airmass storms are small-scale, unorganized convective elements of limited duration and geographic range. Any convective element (i.e., cell) can be categorized as part of an organized storm or an individual airmass storm.
The storm tracking community has attempted for years to determine not only storm advection (i.e., the horizontal motion due to air currents) but also the growth and decay of the organized storm with no significant success. The lifetime of individual cells within the organized storm can be 20 minutes or less. New cells frequently grow near old decaying cells, but not necessarily along the direction of motion of the old cells. As a result of this discrete propagation (i.e., growth and decay process), storm motion can appear to deviate from cellular advection. For short term predictions (e.g., 20 minutes or less), the tracking of cell motion yields accurate predictions. Longer term predictions (e.g., greater than 20 minutes) based on the tracking of cell motion are considerably less accurate.
Tracking the large scale motion, or envelope, of the organized storm is one method that have been used to improve short term forecast accuracy. See, for example, U.S. Pat. No. 5,959,567 in which the method for tracking organized storms is based on the principle that large scale storms tend to decorrelate less rapidly with time. The method includes applying an image filter to a weather radar image to obtain only the large scale features within the image. The large scale features are tracked to determine the motion of the envelope and to generate a predicted image. Image filters that approximate the storm envelope are preferred because they typically yield predicted images with higher accuracy.
Although predicted images are useful for determining the future location of organized storms, it is desirable to determine the predictability, or likelihood, that the specific features within the predicted image will occur. Predictability can provide a level of confidence for reliance on the predicted image for planning future activities.
The invention relates to a method and apparatus for determining the predictability of a selected element within a weather radar image. The method is an improvement on known methodologies for tracking large scale features in weather radar images, and can be implemented using various image processing techniques. The images generated by the method of the present invention provide an indication of the predictability, or likelihood, that the features in predicted weather radar images will happen. The method is based on the principle that large scale storms tend to decorrelate less rapidly with time. By taking weather radar data and filtering it to obtain only the large scale features, the envelope of the organized storm can be determined. Thus, elements in a weather radar image that correspond to elements within the envelope of the organized storm in the filtered image are generally more predictable than other elements within the weather radar image. The predictability of a selected element can be generated from its filtered value and the variation in the values of the element and its neighboring elements.
The method includes receiving a selected element and neighboring elements from a weather radar image and processing the selected and neighboring elements to generate a processed selected element value. The weather radar image can be a reflectivity image and/or an image derived from weather radar image data. In one embodiment, the derived image is the correlation of two other weather radar images representing two different times. In one embodiment, the processing includes filtering the selected element and the neighboring elements. Optionally, filtering includes iteratively rotating an image filter in coordinate space and applying the filter to the selected element and the neighboring elements.
The method also includes the steps of determining a variability from the selected element and the neighboring elements and generating the predictability of the selected element from the processed selected element value and the variability. The variability can be the standard deviation or the variance of the selected element and the neighboring element. According to one embodiment, the predictability is proportional to a power of the processed element. In other embodiments, the method also includes displaying the predictability.
The apparatus for determining a characteristic of a selected weather radar image element includes an input module for receiving the selected element and neighboring elements, a prediction module and an output module for displaying the characteristic. The prediction module includes a rotation module for iteratively rotating a filter in coordinate space and a filter module for applying each rotated filter to the selected element and its neighboring elements. The prediction module also includes a processor module for determining the characteristic from the selected filtered element values and the sets of filtered element values. In one embodiment, the characteristic is a predictability value. In another embodiment, the output module is a graphical display.